class_docstr: Linear warmup and then cosine decay.
  Based on https://huggingface.co/ implementation.
class_docstr_tok: ["Linear","warmup","and","then","cosine","decay",".","Based","on","https",":","\/\/huggingface.co\/","implementation","."]
code: def get_lr(self):
      current_lr = [
          base_lr * lmbda(self.last_epoch)
          for lmbda, base_lr in zip(self.lr_lambdas, self.base_lrs)
      ]
      if self.last_epoch < self.warmup_steps:
          return current_lr
      else:
          return [
              max(self.end_lr, _current_lr)
              for _current_lr in current_lr
          ]
  
file_path: MONAI/monai/optimizers/lr_scheduler.py
imports: from __future__ import annotations
  import math
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import LambdaLR, _LRScheduler
label_desc: Use image node_id 3 for calling the WarmupCosineSchedule obj's underlying member method code with example usage: obj.get_lr() and returns: current_lr, unknown